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[jira] Assigned: (HADOOP-3113) Provide a configurable way for DFSOututStream.flush() to flush data to real block file on DataNode.

Date

Sun, 30 Mar 2008 07:27:24 GMT

[ https://issues.apache.org/jira/browse/HADOOP-3113?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
dhruba borthakur reassigned HADOOP-3113:
----------------------------------------
Assignee: dhruba borthakur
> Provide a configurable way for DFSOututStream.flush() to flush data to real block file
on DataNode.
> ---------------------------------------------------------------------------------------------------
>
> Key: HADOOP-3113
> URL: https://issues.apache.org/jira/browse/HADOOP-3113
> Project: Hadoop Core
> Issue Type: Bug
> Components: dfs
> Reporter: dhruba borthakur
> Assignee: dhruba borthakur
>
> DFSOutputStream has a method called flush() that persists block locations on the namenode
and sends all outstanding data to all datanodes in the pipeline. However, this data goes to
the tmp file on the datanode(s). When the block is closed, the tmp files is renamed to be
the real block file. If the datanode(s) dies before the block is compete, then entire block
is lost. This behaviour wil be fixed in HADOOP-1700.
> However, in the short term, a configuration paramater can be used to allow datanodes
to write to the real block file directly, thereby avoiding writing to the tmp file. This means
that data that is flushed successfully by a client does not get lost even if the datanode(s)
or client dies.
> The Namenode already has code to pick the largest replica (if multiple datanodes have
different sizes of this block). Also, the namenode has code to not trigger replication request
if the file is still being written to.
> The only caveat that I can think of is that the block report periodicity should be much
much smaller that the lease timeout period. A block report adds the being-written-to blocks
to the blocksMap thereby avoiding any cleanup that a lease expiry processing might have otherwise
done.
> Not all requirements specified by HADOOP-1700 are supported by this approach, but it
could still be helpful (in the short term) for a wide range of applications.
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